1. Technical Field
The present disclosure relates to customer support and more specifically to predicting which customers or support requests are more likely to be escalated, so support resources can be devoted to proactively prevent escalation.
2. Introduction
Modern customer service, customer contact, or support centers support large numbers of customers, such as a call center that supports 10,000 or more users. In such large-scale customer support efforts, prioritizing the individuals' needs and appropriately allocating limited available support resources to the most important matters can be crucial. Support resource can include, for example, customer service agents, developers, money, etc. In traditional customer support centers, louder customers typically receive more attention. Otherwise, a service request goes through different levels of a customer service hierarchy. For example, the customer can start out with a low-level agent who attempts to address the customer's problem based on a rote or scripted troubleshooting routine. If the problem is not solved, the low-level agent can escalate the customer or matter to a tier 1 supervisor. The tier 1 supervisor investigates the matter, if the problem is still not solved, can escalate the matter further to a tier 2 supervisor. If the tier 2 supervisor cannot solve the issue, he can escalate the matter further, and so forth.
These conventional methods can be slow and inefficient, and can present a wide gap between the service personnel and the developers or engineers. In other words, the developers are often unaware of the customers' needs and wants because the developers are not part of the loop. Moreover, predicting customer service priorities and urgencies based on the analysis of the service tickets may be limited due to limited context information for a given service ticket which does not adequately capture the bigger picture of the customer's general needs.
Customer support infrastructure is generally adapted to respond to individual problems, such as by ordering problems by severity, and do not identify vulnerable systems that may have multiple often low severity issues that can take a long time to resolve. Further, these support systems cannot identify customers systems that are more likely to have other problems that are not known at present. Such customers may need additional attention or support resources to prevent future escalation. Current systems cannot recognize these cases where marshaling resources in anticipation of issues could be instrumental in managing a successful customer relationship, as well as save service costs associated with customer escalation after the fact. Current systems cannot predict how vulnerable customer systems may be in advance.